Solving symbolic reasoning problems that require compositionality and
sy...
Identifying molecules that exhibit some pre-specified properties is a
di...
Using only image-sentence pairs, weakly-supervised visual-textual ground...
Given a textual phrase and an image, the visual grounding problem is def...
Many neural networks for graphs are based on the graph convolution opera...
In open set recognition, a classifier has to detect unknown classes that...
Training RNNs to learn long-term dependencies is difficult due to vanish...
In recent years, deep generative models for graphs have been used to gen...
In recent years the scientific community has devoted much effort in the
...
The effectiveness of recurrent neural networks can be largely influenced...
Learning to solve sequential tasks with recurrent models requires the ab...
Performing machine learning on structured data is complicated by the fac...
Recently, many researchers have been focusing on the definition of neura...
Many machine learning techniques have been proposed in the last few year...
Recurrent neural networks can learn complex transduction problems that
r...
Predicting the completion time of business process instances would be a ...
The ability to know in advance the trend of running process instances, w...
In this paper we present a novel graph kernel framework inspired the by ...